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Functions352 in github.com/MatrixTeam-AI/RAIN

⨍ Functions 352◇ Types & classes 79

↓ 1 callersFunctionsave_video

( imgs, vid_file_name, fps=24)

pipeline_example.py:141

↓ 1 callersMethodset_attention_slice

r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to

src/models/unet_3d_explicit_reference.py:307

↓ 1 callersMethodset_attention_slice

r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to

src/models/unet_3d.py:307

↓ 1 callersMethodset_attention_slice

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to comp

src/models/unet_2d_condition.py:762

↓ 1 callersMethodset_attn_processor

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

src/models/unet_2d_condition.py:701

↓ 1 callersMethodset_fps

src/morpher.py:623

↓ 1 callersFunctiontop_down_affine

Get the bbox image as the model input by affine transform. Args: input_size (dict): The input size of the model. bbox_scale (dict

src/dwpose/onnxpose.py:256

↓ 1 callersMethodtranslate

(self, clique, all)

src/modeling/translation.py:15

(self, skip_check=SKIP_ENGINE_MODEL_CHECK, output_list=[], return_tensor=False, **inputs)

src/modeling/engine_model.py:116

src/dwpose/wholebody.py:16

(self, oriImg, direct_no_cache=False, direct_translations=None)

src/dwpose/__init__.py:54

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:699

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:808

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:873

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:943

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1019

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1090

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1189

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1319

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1430

( self, attn: Attention, hidden_states: torch.FloatTensor, encoder_hidden_stat

src/models/attention_processor.py:1511

( self, attn: "Attention", hidden_states: torch.FloatTensor, encoder_hidden_st

src/models/attention_processor.py:1598

(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs)

src/models/attention_processor.py:1758

(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs)

src/models/attention_processor.py:1833

(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs)

src/models/attention_processor.py:1912

(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs)

src/models/attention_processor.py:1971

( self, attn, hidden_states, encoder_hidden_states=None, attention_mas

src/models/attention_processor.py:2018

( self, attn, hidden_states, encoder_hidden_states=None, attention_mas

src/models/attention_processor.py:2133

( self, attn, x, context, context_mask=None, )

src/models/attention_processor.py:2249

( self, ref_image, pose_images, width, height, video_length,

src/pipeline/pipeline_pose2vid_lcm.py:295

( self, ref_image, pose_images, width, height, video_length,

src/pipeline/pipeline_pose2vid.py:285

src/taesdv/taesdv.py:193

(self, config_path, device=None)

src/morpher.py:34

(self, reference_unet, reference_control_writer)

src/modeling/framed_models.py:104

src/modeling/framed_models.py:138

(self, image_encoder)

src/modeling/framed_models.py:167

(self, lindex, rindex, horizontal_scale=1.0, vertical_scale=1.0, horizontal_transition=0.0, vertical_transitio

src/modeling/translation.py:5

(self, engine_file_path, stream = None, device_int = 0, extra_lock = None)

src/modeling/engine_model.py:55

( self, num_train_timesteps: int = 1000, beta_start: float = 0.00085, beta_end

src/scheduler/scheduler_lcm.py:199

(self, n_in, n_out)

src/taesdv/taesdv.py:29

Initialize pretrained TAESDV on the given device from the given checkpoints.

src/taesdv/taesdv.py:38

(self, video_file_path)

src/taesdv/taesdv.py:173

(self, video_file_path, width_height, fps=30)

src/taesdv/taesdv.py:187

(self, onnx_det_path, onnx_pose_path, providers=None)

src/dwpose/wholebody.py:9

(self, onnx_det_path, onnx_pose_path, providers, damping_step=1, translations=[], detection_size=(768, 768), n

src/dwpose/__init__.py:36

( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels:

src/models/unet_3d_explicit_reference.py:38

( self, train_kv: bool = True, train_q_out: bool = True, hidden_size: Optional

src/models/attention_processor.py:782

src/models/attention_processor.py:937

(self, attention_op: Optional[Callable] = None)

src/models/attention_processor.py:1016

(self, attention_op: Optional[Callable] = None)

src/models/attention_processor.py:1087

src/models/attention_processor.py:1185

( self, train_kv: bool = True, train_q_out: bool = False, hidden_size: Optiona

src/models/attention_processor.py:1291

( self, train_kv: bool = True, train_q_out: bool = True, hidden_size: Optional

src/models/attention_processor.py:1404

(self, slice_size: int)

src/models/attention_processor.py:1508

(self, slice_size)

src/models/attention_processor.py:1595

( self, f_channels: int, zq_channels: int, )

src/models/attention_processor.py:1688

( self, hidden_size: int, cross_attention_dim: Optional[int] = None, rank: int

src/models/attention_processor.py:1724

( self, hidden_size: int, cross_attention_dim: Optional[int] = None, rank: int

src/models/attention_processor.py:1797

( self, hidden_size: int, cross_attention_dim: int, rank: int = 4, att

src/models/attention_processor.py:1876

( self, hidden_size: int, cross_attention_dim: Optional[int] = None, rank: int

src/models/attention_processor.py:1951

(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0)

src/models/attention_processor.py:2007

(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0)

src/models/attention_processor.py:2117

( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channe

src/models/transformer_3d.py:31

( self, unet, mode="write", do_classifier_free_guidance=False, attenti

src/models/mutual_self_attention.py:20

( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channe

src/models/transformer_2d.py:63

( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels:

src/models/unet_3d.py:38

( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels:

src/models/unet_2d_condition.py:161

( self, dim: int, num_attention_heads: int, attention_head_dim: int, d

src/models/attention.py:301

(self, in_channels: int, out_channels: int, act_fn: str)

src/models/unet_2d_blocks.py:202

( self, in_channels: int, temb_channels: int, dropout: float = 0.0, nu

src/models/unet_2d_blocks.py:254

( self, in_channels: int, temb_channels: int, dropout: float = 0.0, nu

src/models/unet_2d_blocks.py:357

( self, in_channels: int, out_channels: int, temb_channels: int, dropo

src/models/unet_2d_blocks.py:511

( self, in_channels: int, out_channels: int, temb_channels: int, dropo

src/models/unet_2d_blocks.py:682

( self, in_channels: int, out_channels: int, prev_output_channel: int,

src/models/unet_2d_blocks.py:780

( self, in_channels: int, temb_channels: int, dropout: float = 0.0, nu

src/models/unet_3d_blocks.py:180

( self, in_channels: int, out_channels: int, temb_channels: int, dropo

src/models/unet_3d_blocks.py:316

( self, in_channels: int, out_channels: int, temb_channels: int, dropo

src/models/unet_3d_blocks.py:505

( self, in_channels: int, out_channels: int, prev_output_channel: int,

src/models/unet_3d_blocks.py:624

( self, conditioning_embedding_channels: int, conditioning_channels: int = 3,

src/models/pose_guider.py:13

( self, channels, use_conv=False, out_channels=None, padding=1, name="conv" )

src/models/resnet.py:94

( self, *, in_channels, out_channels=None, conv_shortcut=False,

src/models/resnet.py:124

( self, in_channels, num_attention_heads=8, num_transformer_block=2, a

src/models/motion_module.py:45

( self, in_channels, num_attention_heads, attention_head_dim, num_laye

src/models/motion_module.py:95

( self, dim, num_attention_heads, attention_head_dim, attention_block_

src/models/motion_module.py:186

( self, attention_mode=None, cross_frame_attention_mode=None, temporal_positio

src/models/motion_module.py:281

( self, vae, image_encoder, reference_unet, denoising_unet, po

src/pipeline/pipeline_pose2vid_lcm.py:30

( self, vae, image_encoder, reference_unet, denoising_unet, po

src/pipeline/pipeline_pose2vid.py:30

src/taesdv/taesdv.py:177

src/scheduler/scheduler_lcm.py:531

src/taesdv/taesdv.py:179

src/modeling/engine_model.py:196

Method_encode_prompt

( self, prompt, device, num_videos_per_prompt, do_classifier_free_guid

src/pipeline/pipeline_pose2vid.py:176

Method_execution_device

src/pipeline/pipeline_pose2vid_lcm.py:92

Method_execution_device

src/pipeline/pipeline_pose2vid.py:92

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/unet_3d_explicit_reference.py:378

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/transformer_3d.py:102

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/transformer_2d.py:209

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/unet_3d.py:378

Method_set_gradient_checkpointing

(self, module, value=False)

src/models/unet_2d_condition.py:833

Methodadd_noise

( self, original_samples: torch.FloatTensor, noise: torch.FloatTensor, timeste

src/scheduler/scheduler_lcm.py:487

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